Are you using the tidyverse with an OMOP Common Data Model?
Interact with your CDM in a pipe-friendly way with CDMConnector.
- Quickly connect to your CDM and start exploring.
- Build data analysis pipelines using familiar dplyr verbs.
- Easily extract subsets of CDM data from a database.
CDMConnector introduces a single R object that represents an OMOP CDM relational database inspired by the dm, DatabaseConnector, and Andromeda packages. The cdm object can be used in dplyr style data analysis pipelines and facilitates interactive data exploration. cdm objects encapsulate references to OMOP CDM tables in a remote RDBMS as well as metadata necessary for interacting with a CDM.
CDMConnector is meant to be the entry point for composable tidyverse
style data analysis operations on an OMOP CDM. A cdm_reference object
behaves like a named list of tables.
- Quickly create a list of references to a subset of CDM tables
- Store connection information for later use inside functions
- Use any DBI driver back-end with the OMOP CDM
See Getting started for more details.
CDMConnector can be installed from CRAN:
install.packages("CDMConnector")
The development version can be installed from GitHub:
# install.packages("devtools")
devtools::install_github("darwin-eu/CDMConnector")
Create a cdm_reference object from any DBI connection. Use the
`cdm_schema argument to point to a particular schema in your database.
library(CDMConnector)
con <- DBI::dbConnect(duckdb::duckdb(), dbdir = eunomia_dir())
cdm <- cdm_from_con(con, cdm_schema = "main")
cdm
## # OMOP CDM reference (tbl_duckdb_connection)
##
## Tables: person, observation_period, visit_occurrence, visit_detail, condition_occurrence, drug_exposure, procedure_occurrence, measurement, observation, death, location, care_site, provider, drug_era, dose_era, condition_era, cdm_source, concept, vocabulary, concept_relationship, concept_synonym, concept_ancestor, drug_strength
A cdm_reference is a named list of table references:
library(dplyr, warn.conflicts = FALSE)
names(cdm)
## [1] "person" "observation_period" "visit_occurrence"
## [4] "visit_detail" "condition_occurrence" "drug_exposure"
## [7] "procedure_occurrence" "measurement" "observation"
## [10] "death" "location" "care_site"
## [13] "provider" "drug_era" "dose_era"
## [16] "condition_era" "cdm_source" "concept"
## [19] "vocabulary" "concept_relationship" "concept_synonym"
## [22] "concept_ancestor" "drug_strength"
Use dplyr verbs with the table references.
tally(cdm$person)
## # Source: SQL [1 x 1]
## # Database: DuckDB 0.6.1 [root@Darwin 21.6.0:R 4.2.2//var/folders/xx/01v98b6546ldnm1rg1_bvk000000gn/T//RtmpcuCHv2/uudgqiyb/cdm.duckdb]
## n
## <dbl>
## 1 2694
Compose operations with the pipe.
cdm$condition_era %>%
left_join(cdm$concept, by = c("condition_concept_id" = "concept_id")) %>%
count(top_conditions = concept_name, sort = TRUE)
## # Source: SQL [?? x 2]
## # Database: DuckDB 0.6.1 [root@Darwin 21.6.0:R 4.2.2//var/folders/xx/01v98b6546ldnm1rg1_bvk000000gn/T//RtmpcuCHv2/uudgqiyb/cdm.duckdb]
## # Ordered by: desc(n)
## top_conditions n
## <chr> <dbl>
## 1 Viral sinusitis 17268
## 2 Acute viral pharyngitis 10217
## 3 Acute bronchitis 8184
## 4 Otitis media 3561
## 5 Osteoarthritis 2694
## 6 Streptococcal sore throat 2656
## 7 Sprain of ankle 1915
## 8 Concussion with no loss of consciousness 1013
## 9 Sinusitis 1001
## 10 Acute bacterial sinusitis 939
## # … with more rows
Run a simple quality check on a cdm.
cdm <- cdm_from_con(con, cdm_tables = c("person", "observation_period"))
validate_cdm(cdm)
## ── CDM v5.3 validation (checking 2 tables) ─────────────────────────────────────
## ✔ cdm table names
## ✔ all row counts > 0
CDMConnector is tested using the following DBI driver backends:
If you encounter a clear bug, please file an issue with a minimal reproducible example on GitHub.
License: Apache 2.0